Hill, NR, Arden, C, Beresford-Hulme, L, Camm, AJ, Clifton, D, Davies, DW, Farooqui, U, Gordon, J, Groves, L, Hurst, M, Lawton, S, Lister, S, Mallen, C, Martin, A-C, McEwan, P, Pollock, KG, Rogers, J, Sandler, B, Sugrue, DM and Cohen, AT (2020) Identification of undiagnosed atrial fibrillation patients using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial. Contemporary Clinical Trials Communications, 99. 106191 - ?. ISSN 2451-8654

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Atrial fibrillation (AF) is associated with an increased risk of stroke, enhanced stroke severity, and other comorbidities. However, AF is often asymptomatic, and frequently remains undiagnosed until complications occur. Current screening approaches for AF lack either cost-effectiveness or diagnostic sensitivity; thus, there is interest in tools that could be used for population screening. An AF risk prediction algorithm, developed using machine learning from a UK dataset of 2,994,837 patients, was found to be more effective than existing models at identifying patients at risk of AF. Therefore, the aim of the trial is to assess the effectiveness of this risk prediction algorithm combined with diagnostic testing for the identification of AF in a real-world primary care setting. Eligible participants (aged ≥30 years and without an existing AF diagnosis) registered at participating UK general practices will be randomised into intervention and control arms. Intervention arm participants identified at highest risk of developing AF (algorithm risk score ≥ 7.4%) will be invited for a 12‑lead electrocardiogram (ECG) followed by two-weeks of home-based ECG monitoring with a KardiaMobile device. Control arm participants will be used for comparison and will be managed routinely. The primary outcome is the number of AF diagnoses in the intervention arm compared with the control arm during the research window. If the trial is successful, there is potential for the risk prediction algorithm to be implemented throughout primary care for narrowing the population considered at highest risk for AF who could benefit from more intensive screening for AF. Trial Registration: NCT04045639.

Item Type: Article
Additional Information: This is the final published version (version of record). It was first published online via Elsevier at http://doi.org/10.1016/j.cct.2020.106191 - please refer to any applicable terms of use of the publisher.
Uncontrolled Keywords: Atrial fibrillation, Atrial fibrillation screening, Machine learning, Neural networks, Stroke prevention, Targeted screening
Subjects: R Medicine > RC Internal medicine > RC666 Diseases of the circulatory (Cardiovascular) system
Divisions: Faculty of Medicine and Health Sciences > School of Medicine
Related URLs:
Depositing User: Symplectic
Date Deposited: 28 Oct 2020 15:36
Last Modified: 16 Jun 2021 14:54
URI: https://eprints.keele.ac.uk/id/eprint/8838

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